Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Azure Machine Learning Services
Chapter 1: Building an End-To-End Machine Learning Pipeline in Azure
	Performing descriptive data exploration
		Moving data to the cloud
		Understanding missing values
		Visualizing data distributions
		Finding correlated dimensions
		Measuring feature and target dependencies for regression
		Visualizing feature and label dependency for classification
	Exploring common techniques for data preparation
		Labeling the training data
		Normalization and transformation in machine learning
		Encoding categorical variables
		A feature engineering example using time-series data
		Using NLP to extract complex features from text
	Choosing the right ML model to train data
		Choosing an error metric
		The training and testing split
		Achieving great performance using tree-based ensemble models
		Modeling large and complex data using deep learning techniques
	Optimization techniques
		Hyperparameter optimization
		Model stacking
		AutoML
	Deploying and operating models
		Batch scoring using pipelines
		Real-time scoring using a container-based web service
		Tracking model performance, telemetry, and data skew
	Summary
Chapter 2: Choosing a Machine Learning Service in Azure
	Demystifying the different Azure services for ML
		Choosing an Azure service for ML
		Choosing a compute target for an Azure ML service
	Azure Cognitive Services and Custom Vision
		Azure Cognitive Services
		Custom Vision—customizing the Cognitive Services API
	Azure ML tools with GUIs
		Azure ML Studio (classic)
		Azure Automated ML
		Microsoft Power BI
	The Azure ML service
		Organizing experiments and models in Azure ML
		Deployments through Azure ML
	Summary
Section 2: Experimentation and Data Preparation
Chapter 3: Data Experimentation and Visualization Using Azure
	Preparing your Azure ML workspace
		Setting up the ML Service workspace
		Running a simple experiment with Azure ML
		Logging metrics and tracking results
		Scheduling and running scripts
		Adding cloud compute to the workspace
	Visualizing high-dimensional data
		Tracking figures in experiments in Azure ML
		Unsupervised dimensionality reduction with PCA
		Using LDA for supervised projections
		Non-linear dimension reduction with t-SNE
		Generalizing t-SNE with UMAP
	Summary
Chapter 4: ETL, Data Preparation, and Feature Extraction
	Managing data and dataset pipelines in the cloud
		Getting data into the cloud
			Organizing data in data stores and datasets
		Managing data in Azure ML
			Versioning datasets and dataset definitions
			Taking data snapshots for reproducibility
			The life cycle of a dataset
		Exploring data registered in the Azure ML service
			Exploring the datasets
			Exploring the data
	Preprocessing and feature engineering with Azure ML DataPrep  
		Parsing different data formats
			Loading delimiter-separated data
			Parsing JSON data
			Loading binary column-store data in Parquet format
		Building a data transformation pipeline in Azure ML
			Generating features through expression
			Data type conversions
			Deriving columns by example
			Imputing missing values
			Label and one-hot encoding
			Transformations and scaling
			Filtering columns and rows
		Writing the processed data back to a dataset
	Summary
Chapter 5: Advanced Feature Extraction with NLP
	Understanding categorical data
		Comparing textual, categorical, and ordinal data
		Transforming categories into numeric values
			Orthogonal embedding using one-hot encoding
		Categories versus text
	Building a simple bag-of-words model
		A naive bag-of-words model using counting
		Tokenization – turning a string into a list of words
		Stemming – rule-based removal of affixes
		Lemmatization – dictionary-based word normalization
		A bag-of-words model in scikit-learn
	Leveraging term importance and semantics
		Generalizing words using n-grams and skip-grams
		Reducing word dictionary size using SVD
		Measuring the importance of words using tf-idf
		Extracting semantics using word embeddings
	Implementing end-to-end language models
		End-to-end learning of token sequences
		State-of-the-art sequence-to-sequence models
		Text analytics using Azure Cognitive Services
	Summary
Section 3: Training Machine Learning Models
Chapter 6: Building ML Models Using Azure Machine Learning
	Working with tree-based ensemble classifiers
		Understanding a simple decision tree
			Advantages of a decision tree
			Disadvantages of a decision tree
		Combining classifiers with bagging
		Optimizing classifiers with boosting rounds
	Training an ensemble classifier model using LightGBM
		LightGBM in a nutshell
		Preparing the data
		Setting up the compute cluster and execution environment
		Building a LightGBM classifier
		Scheduling the training script on the Azure ML cluster
	Summary
Chapter 7: Training Deep Neural Networks on Azure
	Introduction to deep learning
		Why DL?
		From neural networks to DL
		Comparing classical ML and DL
	Training a CNN for image classification
		Training a CNN from scratch in your notebook
		Generating more input data using augmentation
		Moving training to a GPU cluster using Azure ML compute
		Improving your performance through transfer learning
	Summary
Chapter 8: Hyperparameter Tuning and Automated Machine Learning
	Hyperparameter tuning to find the optimal parameters
		Sampling all possible parameter combinations using grid search
		Trying random combinations using random search
		Converging faster using early termination
			The median stopping policy
			The truncation selection policy
			The bandit policy
			A HyperDrive configuration with termination policy
		Optimizing parameter choices using Bayesian optimization
	Finding the optimal model with AutoML
		Advantages and benefits of AutoML
		A classification example
	Summary
Chapter 9: Distributed Machine Learning on Azure ML Clusters
	Exploring methods for distributed ML
		Training independent models on small data in parallel
		Training a model ensemble on large datasets in parallel
		Fundamental building blocks for distributed ML
		Speeding up DL with data-parallel training
		Training large models with model-parallel training
	Using distributed ML in Azure
		Horovod—a distributed DL training framework
		Implementing the HorovodRunner API for a Spark job
		Running Horovod on Azure ML compute
	Summary
Chapter 10: Building a Recommendation Engine in Azure
	Introduction to recommender engines
	Content-based recommendations
		Measuring similarity between items
		Feature engineering for content-based recommenders
		Content-based recommendations using gradient boosted trees
	Collaborative filtering—a rating-based recommendation engine
		What is a rating? Explicit feedback as opposed to implicit feedback
		Predicting the missing ratings to make a recommendation
		Scalable recommendations using ALS factorization
	Combining content and ratings in hybrid recommendation engines
		Building a state-of-the-art recommender using the Matchbox Recommender
	Automatic optimization through reinforcement learning
		An example using Azure Personalizer in Python
	Summary
Section 4: Optimization and Deployment of Machine Learning Models
Chapter 11: Deploying and Operating Machine Learning Models
	Deploying ML models in Azure
		Understanding the components of an ML model
		Registering your models in a model registry
		Customizing your deployment environment
		Choosing a deployment target in Azure
	Building a real-time scoring service
	Implementing a batch scoring pipeline
	Inference optimizations and alternative deployment targets
		Profiling models for optimal resource configuration
		Portable scoring through the ONNX runtime
		Fast inference using FPGAs in Azure
		Alternative deployment targets
	Monitoring Azure ML deployments
		Collecting logs and infrastructure metrics
		Tracking telemetry and application metrics
	Summary
Chapter 12: MLOps - DevOps for Machine Learning
	Ensuring reproducible builds and deployments
		Version-controlling your code
		Registering snapshots of your data
		Tracking your model metadata and artifacts
		Scripting your environments and deployments
	Validating your code, data, and models
		Rethinking unit testing for data quality
		Integration testing for ML
		End-to-end testing using Azure ML
		Continuous profiling of your model
	Summary
Chapter 13: What\'s Next?
	Understanding the importance of data
	The future of ML is automated
	Change is the only constant – preparing for change
	Focusing first on infrastructure and monitoring
	Controlled rollouts and A/B testing
	Summary
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